Of the various approaches to modeling the statistical dependences between multiple spike-train data, all require certain assumptions about the relationships within the data. However, with the recent increase in accessibility of datasets containing simultaneously recorded spike-train data, it is now possible to reconsider, and test, the effectiveness of different novel methods for extracting functional dependences at the neuronal level. Two of the most commonly used methods are Generalized Linear Models (GLMs) and Latent Variable Models (LVMs). GLMs have the benefit of being able to take into account stimuli, self-spiking history, and outputs of the trigger neurons [1]}, [2]}, [3]}, [4]}, [5]}. While the LVMs can also include self-spiking history and external stimuli, it assumes that multiple neural activities arise from some common hidden sources. Accordingly, this method can provide us with a low dimensional representation of observed spiking activities. Nonetheless, inferring the posterior distribution of the latent variables and estimating model parameters can be difficult. cite in the literature there has been a great deal of discussion as to how best to accurately and efficiently infer and learn the LVMs [6]}, [7]}, [8]}, [9]}.
